GEO series

Heterogeneity in populations from subcutaneous abdominal and thigh adipose tissues of women

GSE306825 Homo sapiens (human) Single-nucleus RNA-seq 14 samples Submitted 2025/12/15 Platform GPL24676

At a glance

Single-nucleus RNA-seq · human · 14 samples

What it is: Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue). Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Homo sapiens (human)
Samples: 14 samples
Data you can get: A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.

Summary (written by the submitters)

Women with a ‘pear-shaped’ fat distribution are at lower risk to develop insulin resistance and type 2 diabetes, but the mechanisms involved remain poorly understood. The objective of this study was to determine if the adipose cell populations differed between abdominal subcutaneous adipose tissue (ASAT) and lower body thigh (TSAT) adipose tissue of clinically healthy, premenopausal women with body mass indices (BMI) in the non-obese to obese range.

Read the full summary ↓

Paired samples of A- and T- SAT were taken under local lidocaine anaesthesia and quick frozen in lquid nitrogen. Single nuclei RNA sequencing (snRNASeq) was used to identify and major cell populations and their subsclusters. We found differences in subcluster cell numbers and identified differentially expressed genestthat may contribute to depot differences in metabolism as well as expansion and homeostatic remodeling capacities through cell cell communucation. These variations likely contribute to protective effect of lower body fat on metabolic health and T2D risk.

This dataset

Open GSE306825 in finder → Find more human RNA-seq datasets →

Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE306825_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1312459 and SRA study SRP614636. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Samples in this study

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+ 14 more — browse all 14 samples with per-sample file links →

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GSE245948 A dynamic subcutaneous adipose transcriptome of human insulin resistance identifies a molecular predisposition shared with visceral adipose tissue 76 samples shares subcutaneous, adipose
GSE302701 Single-nucleus RNA sequencing of human subcutaneous adipose tissue biopsies from 59 Finnish participants. 59 samples shares subcutaneous, adipose
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GSE243375 HER2 heterogeneity and treatment response-associated profiles in HER2-positive breast cancer in the NCT02326974 clinical trial 287 samples shares heterogeneity

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.